Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fixup OpenAI Embeddings - fix the weighted mean #3778

Merged
merged 1 commit into from
May 1, 2023
Merged

Fixup OpenAI Embeddings - fix the weighted mean #3778

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 6 additions & 4 deletions langchain/embeddings/openai.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ def validate_environment(cls, values: Dict) -> Dict:
def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for i in range(len(texts))]
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken

Expand Down Expand Up @@ -180,10 +180,10 @@ def _get_len_safe_embeddings(
batched_embeddings += [r["embedding"] for r in response["data"]]

results: List[List[List[float]]] = [[] for _ in range(len(texts))]
lens: List[List[int]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
lens[indices[i]].append(len(batched_embeddings[i]))
num_tokens_in_batch[indices[i]].append(len(tokens[i]))

for i in range(len(texts)):
_result = results[i]
Expand All @@ -192,7 +192,9 @@ def _get_len_safe_embeddings(
"data"
][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=lens[i])
average = np.average(
_result, axis=0, weights=num_tokens_in_batch[i]
)
embeddings[i] = (average / np.linalg.norm(average)).tolist()

return embeddings
Expand Down